Optimize Budget Allocation

Optimize budget allocation across campaigns using linear programming to maximize conversions.

⚠️ IMPORTANT: This tool retrieves READ-ONLY optimization recommendations. Safe to call multiple times. Does NOT automatically change budgets.

🎯 What This Tool Does (Performance Agent - Phase 1):

• Uses linear programming to optimize budget distribution
• Maximizes total conversions while respecting constraints
• Provides current vs optimized allocation comparison
• Categorizes campaigns into actions: PAUSE/SCALE/REDUCE/MAINTAIN
• Shows expected conversion lift from optimization
• Generates specific recommendations with budget amounts

Returns detailed optimization plan:

• Current allocation (what you have now)
• Optimized allocation (what you should have)
• Expected conversion lift (absolute and percentage)
• Campaign-by-campaign actions with reasoning
• Budget change amounts and percentages
• Specific implementation recommendations

Optimization Algorithm: Uses scipy linear programming with constraints:

1. Sum of budgets = total_budget (you don’t overspend)
2. Only campaigns with ROAS >= target get significant budget
3. Min budget per campaign >= min_daily_budget (or $0 to pause)
4. Max change per campaign <= ±max_change_percentage (avoid drastic shifts)

Target ROAS Resolution (3-tier priority):

1. User override (if target_roas parameter provided)
2. Account goals table (user-set or API-pulled)
3. 90-day historical average ROAS
4. Default to 1.0x (breakeven) if no data

Configuration Parameters:

• total_budget: Total monthly budget to allocate (REQUIRED)
• lookback_days: 7, 30, 60, 90, or 120 days (default: 30)
• start_date: Optional start date (YYYY-MM-DD). Overrides lookback_days when used with end_date.
• end_date: Optional end date (YYYY-MM-DD). Overrides lookback_days when used with start_date.
• target_roas: Optional override (e.g., 3.0 for 3.0x)
• max_change_percentage: 0.0-1.0 (default: 0.5 = ±50%)
  • 0.3 = Conservative (±30% change, minimal disruption)
  • 0.5 = Balanced (±50% change, standard optimization)
  • 0.7 = Aggressive (±70% change, fast scaling)
• min_daily_budget: Minimum $per campaign (default:$5.00, or $0.00 to allow pausing)
• customer_id: Optional (uses connected account if omitted)

Execution time: 1-5 seconds (depends on campaign count) Data source: campaign_daily_metrics table (updated nightly)

Use this tool when:

• User wants to optimize budget allocation
• User asks “how should I allocate my budget?”
• User wants to maximize conversions with current spend
• User wants data-driven budget recommendations
• After running wasted spend analysis (natural next step)

📊 AFTER calling this tool, help the user understand:

Campaign Actions:

• PAUSE: Campaigns below target ROAS, losing money (ROAS < target)
• SCALE: High performers, increase budget by X% (top conversion rates)
• REDUCE: Underperformers, decrease budget by X% (low efficiency)
• MAINTAIN: Steady performers, keep current budget (±5% change)

Expected Impact:

• Current conversions: What you get now
• Optimized conversions: What you could get
• Conversion lift: Additional conversions (+X%)

Example Interpretation: “Implementing this optimization will increase your conversions by 45 (+18.8%) without spending more money. You should scale ‘Brand - Exact’ campaign by $2,250/month and pause 'Display - Broad' to free up$3,000/month.”

Implementation Steps:

1. Review recommended changes carefully
2. Start with campaigns marked CRITICAL (pause/scale first)
3. Apply changes gradually if user is risk-averse
4. Monitor performance for 7-14 days after changes
5. Re-run optimization monthly for continuous improvement

Important Notes:

• This is a RECOMMENDATION tool, not automated budget application
• User must review and apply changes manually in Google Ads
• Avoid large changes (>50%) for campaigns in learning phase (<7 days)
• Consider seasonality when interpreting results
• Re-optimize every 30 days as performance changes

Visualization Tip: For 5+ campaigns, suggest creating a grouped bar chart showing current vs optimized budgets side-by-side.

Best Practices:

• Start conservative (max_change_percentage=0.3) for first optimization
• Increase aggressiveness (0.5-0.7) as you gain confidence
• Use longer lookback_days (60-90) during seasonal changes
• Set realistic target_roas (start with 1.0x breakeven, increase gradually)

💬 Community: For optimization discussions, visit our Discord: https://discord.gg/dH3Qt4YS